We also compare against the state-of-the-art methods in terms of the maintained model size and the extracted mesh accuracy. For RoutedFusion [1]}, we use a voxel size of 2cm, corresponding to a grid resolution of \(512^3\)  allocated voxels. For LIG [2]}, we use a part size of 25cm to meet the point density. As shown in Fig. REF , the continuous characteristic of our signed distance function leads to spurious zero level-set surfaces in unseen areas, hence resulting in low reconstruction accuracy. However, if we follow the competitive methods to reason the occupancy status of each voxel grid according to previous observations and only extract triangle meshes around occupied voxels, the overall accuracy outperforms the state-of-the-art methods (Tab. REF ). It should be noted that we only maintain a single network with a size of less than 800 KB to achieve \(mm\)  level of accuracy through continual learning. This is consistent with our average accuracy of SDF approximation in Fig. REF .
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